Data Manipulation with Dplyr

GOAL: To be able to use the 6 data frame manipulation in dplyr.

• we select certain observations (rows) or variables (columns)
• we often group the data by a certain variable(s), or we even calculate summary statistics
• we can do these operations using the normal base R operations:

Load data

setwd('~/workshops/data-workshops/notebooks/dplyr-tidyr/')
#gapminder <- read.csv("https://goo.gl/BtBnPg", header = T)
gapminder <- read.csv("data/gapminder.csv", header = T)

Manipulation of dataframes means many things to many researchers,

• we often select certain observations (rows) or variables (columns),
• we often group the data by a certain variable(s),
• or we even calculate summary statistics.

We can do these operations using the normal base R operations:

mean(gapminder[gapminder$continent == "Africa", "gdpPercap"])

## [1] 2193.755

mean(gapminder[gapminder$continent == "Americas", "gdpPercap"])

## [1] 7136.11

mean(gapminder[gapminder$continent == "Asia", "gdpPercap"])

## [1] 7902.15

• but not great b/c there’s a fair amount of repetition.
• Repeating yourself will cost you time, both now and later, and potentially introduce some nasty bugs.

dplyr package

package provides a number of very useful functions for manipulating dataframes in a way that will * reduces repetition * introduces functions to help * dplyr grammar easier to read

Here we’re going to cover 6 of the most commonly used functions as well as using pipes (%>%) to combine them.

1. select()
2. filter()
3. group_by()
4. summarize()
5. mutate()

Install this package:

#install.packages('dplyr')
library(dplyr)

## 
## Attaching package: 'dplyr'

## The following objects are masked from 'package:stats':
## 
##     filter, lag

## The following objects are masked from 'package:base':
## 
##     intersect, setdiff, setequal, union

head(gapminder)

Using select()

If we want to select just a few variables in our data frame.

year_country_gdp <- select(gapminder, year, country, gdpPercap)

Show fig 1 in lesson.

* If we open up year_country_gdp we’ll see that it only contains the year, country and gdpPercap. Above we used ‘normal’ grammar, but the strengths of dplyr lie in combining several functions using pipes. Since the pipes grammar is unlike anything we’ve seen in R before, let’s repeat what we’ve done above using pipes.

Using pipes (%>%), forward pipe left to right, obviates need to include dataframe

year_country_gdp <- gapminder %>% select(year,country,gdpPercap)
#base-r way
year_country_gdp_br <- gapminder[, c("year", "country", "gdpPercap")]

If we now wanted to move forward with the above, but only with European countries, we can combine select and filter

Filter

year_country_gdp_euro <- gapminder %>%
  filter(continent == "Europe") %>%
  select(year, country, gdpPercap)

http://swcarpentry.github.io/r-novice-gapminder/13-dplyr#challenge-1

• above notice we pass the data from filter to select
• order of operations matter

Using group_by() and summarize()

• Now, we were supposed to be reducing the error prone repetitiveness of what can be done with base R, but up to now we haven’t done that since we would have to repeat the above for each continent.
• Instead of filter(), which will only pass observations that meet your criteria (in the above: continent==“Europe”), we can use group_by(), which will essentially use every unique criteria that you could have used in filter.

str(gapminder)

## 'data.frame':    1704 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ pop      : num  8425333 9240934 10267083 11537966 13079460 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ gdpPercap: num  779 821 853 836 740 ...

str(gapminder %>% group_by(continent))

## Classes 'grouped_df', 'tbl_df', 'tbl' and 'data.frame':  1704 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ pop      : num  8425333 9240934 10267083 11537966 13079460 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ gdpPercap: num  779 821 853 836 740 ...
##  - attr(*, "vars")=List of 1
##   ..$ : symbol continent
##  - attr(*, "drop")= logi TRUE
##  - attr(*, "indices")=List of 5
##   ..$ : int  24 25 26 27 28 29 30 31 32 33 ...
##   ..$ : int  48 49 50 51 52 53 54 55 56 57 ...
##   ..$ : int  0 1 2 3 4 5 6 7 8 9 ...
##   ..$ : int  12 13 14 15 16 17 18 19 20 21 ...
##   ..$ : int  60 61 62 63 64 65 66 67 68 69 ...
##  - attr(*, "group_sizes")= int  624 300 396 360 24
##  - attr(*, "biggest_group_size")= int 624
##  - attr(*, "labels")='data.frame':   5 obs. of  1 variable:
##   ..$ continent: Factor w/ 5 levels "Africa","Americas",..: 1 2 3 4 5
##   ..- attr(*, "vars")=List of 1
##   .. ..$ : symbol continent
##   ..- attr(*, "drop")= logi TRUE

• You will notice that the structure of the dataframe where we used group_by() (grouped_df) is not the same as the original gapminder (data.frame).
• A grouped_df can be thought of as a list where each item in the listis a data.frame which contains only the rows that correspond to the a particular value continent (at least in the example above).

gapminder-group

Using summarize()

• The above was a bit on the uneventful side because group_by() much more exciting in conjunction with summarize()
• This will allow use to create new variable(s) by using functions that repeat for each of the continent-specific data frames.
• using the group_by() function, we split our original dataframe into multiple pieces, then we can run functions (e.g. mean() or sd()) within summarize().

gdp_bycontinents <- gapminder %>%
    group_by(continent) %>%
    summarize(mean_gdpPercap=mean(gdpPercap))

gap-summarize

• That allowed us to calculate the mean gdpPercap for each continent, but it gets even better.

http://swcarpentry.github.io/r-novice-gapminder/13-dplyr#challenge-2

• The function group_by() allows us to group by multiple variables. Let’s group by year and continent.

gdp_bycontinents_byyear <- gapminder %>%
    group_by(continent,year) %>%
    summarize(mean_gdpPercap=mean(gdpPercap))

• That is already quite powerful, but it gets even better! You’re not limited to defining 1 new variable in summarize().

gdp_pop_bycontinents_byyear <- gapminder %>%
    group_by(continent,year) %>%
    summarize(mean_gdpPercap=mean(gdpPercap),
              sd_gdpPercap=sd(gdpPercap),
              mean_pop=mean(pop),
              sd_pop=sd(pop))

Using mutate()

• We can also create new variables prior to (or even after) summarizing information using mutate().

gdp_pop_bycontinents_byyear <- gapminder %>%
    mutate(gdp_billion=gdpPercap*pop/10^9) %>%
    group_by(continent,year) %>%
    summarize(mean_gdpPercap=mean(gdpPercap),
              sd_gdpPercap=sd(gdpPercap),
              mean_pop=mean(pop),
              sd_pop=sd(pop),
              mean_gdp_billion=mean(gdp_billion),
              sd_gdp_billion=sd(gdp_billion))

http://swcarpentry.github.io/r-novice-gapminder/13-dplyr#advanced-challenge
https://www.rstudio.com/wp-content/uploads/2015/02/data-wrangling-cheatsheet.pdf

Tidyr

Researchers often want to manipulate their data from the ‘wide’ to the ‘long’ format, or vice-versa. The ‘long’ format is where:

• each column is a variable

• each row is an observation

• In the ‘long’ format, you usually have 1 column for the observed variable and the other columns are ID variables.

• For the ‘wide’ format each row is often a site/subject/patient and you have multiple observation variables containing the same type of data.
• These can be either repeated observations over time, or observation of multiple variables (or a mix of both).
• You may find data input may be simpler or some other applications may prefer the ‘wide’ format.

• However, many of R’s functions have been designed assuming you have ‘long’ format data. This tutorial will help you efficiently transform your data regardless of original format.

widevlong

• These data formats mainly affect readability.
• For humans, the wide format is often more intuitive since we can often see more of the data on the screen due to it’s shape.
• However, the long format is more machine readable and is closer to the formatting of databases.
• The ID variables in our dataframes are similar to the fields in a database and observed variables are like the database values.

#install.packages("tidyr")
#install.packages("dplyr")

• Load the packages

library("tidyr")
library("dplyr")

• let’s look at the structure of gapminder

str(gapminder)

## 'data.frame':    1704 obs. of  6 variables:
##  $ country  : Factor w/ 142 levels "Afghanistan",..: 1 1 1 1 1 1 1 1 1 1 ...
##  $ year     : int  1952 1957 1962 1967 1972 1977 1982 1987 1992 1997 ...
##  $ pop      : num  8425333 9240934 10267083 11537966 13079460 ...
##  $ continent: Factor w/ 5 levels "Africa","Americas",..: 3 3 3 3 3 3 3 3 3 3 ...
##  $ lifeExp  : num  28.8 30.3 32 34 36.1 ...
##  $ gdpPercap: num  779 821 853 836 740 ...

http://swcarpentry.github.io/r-novice-gapminder/14-tidyr#challenge-1

• Sometimes, as with the gapminder dataset, we have multiple types of observed data.
• It is somewhere in between the purely ‘long’ and ‘wide’ data formats.
• we have 3 “ID variables” (continent, country, year) and 2 “observation variables” (pop,lifeExp,gdpPercap)
• this intermediate format in most cases despite not having ALL observations in 1 column given that all 3 observation variables have different units.

• There are few operations that would need us to stretch out this dataframe any longer (i.e. 4 ID variables and 1 Observation variable).

• which are often vector based, you usually do not want to do mathematical operations on values with different units.
• using the purely long format, a single mean for all of the values of population, life expectancy, and GDP would not be meaningful since it would return the mean of values with 3 incompatible units.
• The solution is that we first manipulate the data either by grouping (see the lesson on dplyr), or we change the structure of the dataframe.

• Note: Some plotting functions in R actually work better in the wide format data.

From wide to long format with gather()

• Until now, we’ve been using the nicely formatted original gapminder dataset, but ‘real’ data (i.e. our own research data) will never be so well organized. Here let’s start with the wide format version of the gapminder dataset.

• We’ll load the data file and look at it. Note: we don’t want our continent and country columns to be factors, so we use the stringsAsFactors argument for read.csv() to disable that.

download.file('https://raw.githubusercontent.com/swcarpentry/r-novice-gapminder/gh-pages/_episodes_rmd/data/gapminder_wide.csv', 'data/gapminder_wide.csv')

gap_wide <- read.csv("data/gapminder_wide.csv", stringsAsFactors = FALSE)
str(gap_wide)

## 'data.frame':    142 obs. of  38 variables:
##  $ continent     : chr  "Africa" "Africa" "Africa" "Africa" ...
##  $ country       : chr  "Algeria" "Angola" "Benin" "Botswana" ...
##  $ gdpPercap_1952: num  2449 3521 1063 851 543 ...
##  $ gdpPercap_1957: num  3014 3828 960 918 617 ...
##  $ gdpPercap_1962: num  2551 4269 949 984 723 ...
##  $ gdpPercap_1967: num  3247 5523 1036 1215 795 ...
##  $ gdpPercap_1972: num  4183 5473 1086 2264 855 ...
##  $ gdpPercap_1977: num  4910 3009 1029 3215 743 ...
##  $ gdpPercap_1982: num  5745 2757 1278 4551 807 ...
##  $ gdpPercap_1987: num  5681 2430 1226 6206 912 ...
##  $ gdpPercap_1992: num  5023 2628 1191 7954 932 ...
##  $ gdpPercap_1997: num  4797 2277 1233 8647 946 ...
##  $ gdpPercap_2002: num  5288 2773 1373 11004 1038 ...
##  $ gdpPercap_2007: num  6223 4797 1441 12570 1217 ...
##  $ lifeExp_1952  : num  43.1 30 38.2 47.6 32 ...
##  $ lifeExp_1957  : num  45.7 32 40.4 49.6 34.9 ...
##  $ lifeExp_1962  : num  48.3 34 42.6 51.5 37.8 ...
##  $ lifeExp_1967  : num  51.4 36 44.9 53.3 40.7 ...
##  $ lifeExp_1972  : num  54.5 37.9 47 56 43.6 ...
##  $ lifeExp_1977  : num  58 39.5 49.2 59.3 46.1 ...
##  $ lifeExp_1982  : num  61.4 39.9 50.9 61.5 48.1 ...
##  $ lifeExp_1987  : num  65.8 39.9 52.3 63.6 49.6 ...
##  $ lifeExp_1992  : num  67.7 40.6 53.9 62.7 50.3 ...
##  $ lifeExp_1997  : num  69.2 41 54.8 52.6 50.3 ...
##  $ lifeExp_2002  : num  71 41 54.4 46.6 50.6 ...
##  $ lifeExp_2007  : num  72.3 42.7 56.7 50.7 52.3 ...
##  $ pop_1952      : num  9279525 4232095 1738315 442308 4469979 ...
##  $ pop_1957      : num  10270856 4561361 1925173 474639 4713416 ...
##  $ pop_1962      : num  11000948 4826015 2151895 512764 4919632 ...
##  $ pop_1967      : num  12760499 5247469 2427334 553541 5127935 ...
##  $ pop_1972      : num  14760787 5894858 2761407 619351 5433886 ...
##  $ pop_1977      : num  17152804 6162675 3168267 781472 5889574 ...
##  $ pop_1982      : num  20033753 7016384 3641603 970347 6634596 ...
##  $ pop_1987      : num  23254956 7874230 4243788 1151184 7586551 ...
##  $ pop_1992      : num  26298373 8735988 4981671 1342614 8878303 ...
##  $ pop_1997      : num  29072015 9875024 6066080 1536536 10352843 ...
##  $ pop_2002      : int  31287142 10866106 7026113 1630347 12251209 7021078 15929988 4048013 8835739 614382 ...
##  $ pop_2007      : int  33333216 12420476 8078314 1639131 14326203 8390505 17696293 4369038 10238807 710960 ...

* The first step towards getting our nice intermediate data format is to first convert from the wide to the long format. * The tidyr function gather() will ‘gather’ your observation variables into a single variable.

gap_long <- gap_wide %>% 
  gather(obstype_year, obs_values, starts_with('pop'), 
starts_with('lifeExp'), starts_with('gdpPercap'))
str(gap_long)

## 'data.frame':    5112 obs. of  4 variables:
##  $ continent   : chr  "Africa" "Africa" "Africa" "Africa" ...
##  $ country     : chr  "Algeria" "Angola" "Benin" "Botswana" ...
##  $ obstype_year: chr  "pop_1952" "pop_1952" "pop_1952" "pop_1952" ...
##  $ obs_values  : num  9279525 4232095 1738315 442308 4469979 ...

• we’ve used piping syntax like we did in dplyr

• in fact, these are compatible and you can use a mix of tidyr and dplyr funcitons togeth

• inside gather() we first name the new column for the new ID variable (obstyper_year), the name for the new amalgamated observation variable (obs_value), then the names of the old observation variables.
• we could have typed out all the observation variables but as in the select() function in dplyr, we can use starts_with() argument to select all variables that start with the desired string

• gather also allows alternate syntax of using the - symbol to identify which variabels are not to be gathered (i.e. ID variables)

longformat

gap_long <- gap_wide %>% gather(obstype_year,obs_values,-continent,-country)
str(gap_long)

## 'data.frame':    5112 obs. of  4 variables:
##  $ continent   : chr  "Africa" "Africa" "Africa" "Africa" ...
##  $ country     : chr  "Algeria" "Angola" "Benin" "Botswana" ...
##  $ obstype_year: chr  "gdpPercap_1952" "gdpPercap_1952" "gdpPercap_1952" "gdpPercap_1952" ...
##  $ obs_values  : num  2449 3521 1063 851 543 ...

• this may seem trival with this dataset, but sometimes you have 1 ID variable and 40 Observations variables with irregular variable names

• flexibility is a big time saver!

• now obstype_year contains 2 pieces of information, the observation type(pop, lifeExp, or gdpPercap) and the year.

• we can use the separate() function to split the character strings into multiple variables

gap_long <- gap_long %>% separate(obstype_year,into=c('obs_type','year'),sep="_")
gap_long$year <- as.integer(gap_long$year)

http://swcarpentry.github.io/r-novice-gapminder/14-tidyr#challenge-2

From long to intermediate format with spread()

• let’s use the opposite of gather to spread our observation variables back out
• we cna then spread gap_long() to the original intermediate format or the widest format
• let’s start with intermediate format

gap_normal <- gap_long %>% spread(obs_type,obs_values)
dim(gap_normal)

## [1] 1704    6

names(gap_normal)

## [1] "continent" "country"   "year"      "gdpPercap" "lifeExp"   "pop"

names(gapminder)

## [1] "country"   "year"      "pop"       "continent" "lifeExp"   "gdpPercap"

Now we’ve got an intermediate dataframe gap_normal with the same dimensions as the original gapminder, but the order of the variables is different. Let’s fix that before checking if they are all.equal().

gap_normal <- gap_normal[,names(gapminder)]
all.equal(gap_normal,gapminder)

## [1] "Component \"country\": Modes: character, numeric"                        
## [2] "Component \"country\": Attributes: < target is NULL, current is list >"  
## [3] "Component \"country\": target is character, current is factor"           
## [4] "Component \"pop\": Mean relative difference: 1.634504"                   
## [5] "Component \"continent\": Modes: character, numeric"                      
## [6] "Component \"continent\": Attributes: < target is NULL, current is list >"
## [7] "Component \"continent\": target is character, current is factor"         
## [8] "Component \"lifeExp\": Mean relative difference: 0.203822"               
## [9] "Component \"gdpPercap\": Mean relative difference: 1.162302"

head(gap_normal)

##   country year      pop continent lifeExp gdpPercap
## 1 Algeria 1952  9279525    Africa  43.077  2449.008
## 2 Algeria 1957 10270856    Africa  45.685  3013.976
## 3 Algeria 1962 11000948    Africa  48.303  2550.817
## 4 Algeria 1967 12760499    Africa  51.407  3246.992
## 5 Algeria 1972 14760787    Africa  54.518  4182.664
## 6 Algeria 1977 17152804    Africa  58.014  4910.417

head(gapminder)

##       country year      pop continent lifeExp gdpPercap
## 1 Afghanistan 1952  8425333      Asia  28.801  779.4453
## 2 Afghanistan 1957  9240934      Asia  30.332  820.8530
## 3 Afghanistan 1962 10267083      Asia  31.997  853.1007
## 4 Afghanistan 1967 11537966      Asia  34.020  836.1971
## 5 Afghanistan 1972 13079460      Asia  36.088  739.9811
## 6 Afghanistan 1977 14880372      Asia  38.438  786.1134

We’re almost there, the original was sorted by country, continent, then year.

gap_normal <- gap_normal %>% arrange(country,continent,year)
all.equal(gap_normal,gapminder)

## [1] "Component \"country\": Modes: character, numeric"                        
## [2] "Component \"country\": Attributes: < target is NULL, current is list >"  
## [3] "Component \"country\": target is character, current is factor"           
## [4] "Component \"continent\": Modes: character, numeric"                      
## [5] "Component \"continent\": Attributes: < target is NULL, current is list >"
## [6] "Component \"continent\": target is character, current is factor"

• great we’ve gone from longest format back to the intermediate and didn’t introcude any errors in our code
• now let’s conver the long all the way back to the wide
• in the wide format, we will keep country and continent as ID variables and spread the observations across the 3 metrics (pop, lifeExp, gdpPercap) and time (year)
• we need appropriate lables for all our new variables (time*metric combinations)
• we also need to unify our ID varialbes to simplify the process of defining gap_wide

gap_temp <- gap_long %>% unite(var_ID,continent,country,sep="_")
str(gap_temp)

## 'data.frame':    5112 obs. of  4 variables:
##  $ var_ID    : chr  "Africa_Algeria" "Africa_Angola" "Africa_Benin" "Africa_Botswana" ...
##  $ obs_type  : chr  "gdpPercap" "gdpPercap" "gdpPercap" "gdpPercap" ...
##  $ year      : int  1952 1952 1952 1952 1952 1952 1952 1952 1952 1952 ...
##  $ obs_values: num  2449 3521 1063 851 543 ...

gap_temp <- gap_long %>%
    unite(ID_var,continent,country,sep="_") %>%
    unite(var_names,obs_type,year,sep="_")
str(gap_temp)

## 'data.frame':    5112 obs. of  3 variables:
##  $ ID_var    : chr  "Africa_Algeria" "Africa_Angola" "Africa_Benin" "Africa_Botswana" ...
##  $ var_names : chr  "gdpPercap_1952" "gdpPercap_1952" "gdpPercap_1952" "gdpPercap_1952" ...
##  $ obs_values: num  2449 3521 1063 851 543 ...

• Using unite() we now have a single ID variable which is a combination of continent,country,and we have defined variable names. We’re now ready to pipe in spread()

gap_wide_new <- gap_long %>%
    unite(ID_var,continent,country,sep="_") %>%
    unite(var_names,obs_type,year,sep="_") %>%
    spread(var_names,obs_values)
str(gap_wide_new)

## 'data.frame':    142 obs. of  37 variables:
##  $ ID_var        : chr  "Africa_Algeria" "Africa_Angola" "Africa_Benin" "Africa_Botswana" ...
##  $ gdpPercap_1952: num  2449 3521 1063 851 543 ...
##  $ gdpPercap_1957: num  3014 3828 960 918 617 ...
##  $ gdpPercap_1962: num  2551 4269 949 984 723 ...
##  $ gdpPercap_1967: num  3247 5523 1036 1215 795 ...
##  $ gdpPercap_1972: num  4183 5473 1086 2264 855 ...
##  $ gdpPercap_1977: num  4910 3009 1029 3215 743 ...
##  $ gdpPercap_1982: num  5745 2757 1278 4551 807 ...
##  $ gdpPercap_1987: num  5681 2430 1226 6206 912 ...
##  $ gdpPercap_1992: num  5023 2628 1191 7954 932 ...
##  $ gdpPercap_1997: num  4797 2277 1233 8647 946 ...
##  $ gdpPercap_2002: num  5288 2773 1373 11004 1038 ...
##  $ gdpPercap_2007: num  6223 4797 1441 12570 1217 ...
##  $ lifeExp_1952  : num  43.1 30 38.2 47.6 32 ...
##  $ lifeExp_1957  : num  45.7 32 40.4 49.6 34.9 ...
##  $ lifeExp_1962  : num  48.3 34 42.6 51.5 37.8 ...
##  $ lifeExp_1967  : num  51.4 36 44.9 53.3 40.7 ...
##  $ lifeExp_1972  : num  54.5 37.9 47 56 43.6 ...
##  $ lifeExp_1977  : num  58 39.5 49.2 59.3 46.1 ...
##  $ lifeExp_1982  : num  61.4 39.9 50.9 61.5 48.1 ...
##  $ lifeExp_1987  : num  65.8 39.9 52.3 63.6 49.6 ...
##  $ lifeExp_1992  : num  67.7 40.6 53.9 62.7 50.3 ...
##  $ lifeExp_1997  : num  69.2 41 54.8 52.6 50.3 ...
##  $ lifeExp_2002  : num  71 41 54.4 46.6 50.6 ...
##  $ lifeExp_2007  : num  72.3 42.7 56.7 50.7 52.3 ...
##  $ pop_1952      : num  9279525 4232095 1738315 442308 4469979 ...
##  $ pop_1957      : num  10270856 4561361 1925173 474639 4713416 ...
##  $ pop_1962      : num  11000948 4826015 2151895 512764 4919632 ...
##  $ pop_1967      : num  12760499 5247469 2427334 553541 5127935 ...
##  $ pop_1972      : num  14760787 5894858 2761407 619351 5433886 ...
##  $ pop_1977      : num  17152804 6162675 3168267 781472 5889574 ...
##  $ pop_1982      : num  20033753 7016384 3641603 970347 6634596 ...
##  $ pop_1987      : num  23254956 7874230 4243788 1151184 7586551 ...
##  $ pop_1992      : num  26298373 8735988 4981671 1342614 8878303 ...
##  $ pop_1997      : num  29072015 9875024 6066080 1536536 10352843 ...
##  $ pop_2002      : num  31287142 10866106 7026113 1630347 12251209 ...
##  $ pop_2007      : num  33333216 12420476 8078314 1639131 14326203 ...

http://swcarpentry.github.io/r-novice-gapminder/14-tidyr#challenge-3

Now we have a great ‘wide’ format dataframe, but the ID_var could be more usable, let’s separate it into 2 variables with separate()

gap_wide_betterID <- separate(gap_wide_new,ID_var,c("continent","country"),sep="_")
gap_wide_betterID <- gap_long %>%
    unite(ID_var, continent,country,sep="_") %>%
    unite(var_names, obs_type,year,sep="_") %>%
    spread(var_names, obs_values) %>%
    separate(ID_var, c("continent","country"),sep="_")
str(gap_wide_betterID)

## 'data.frame':    142 obs. of  38 variables:
##  $ continent     : chr  "Africa" "Africa" "Africa" "Africa" ...
##  $ country       : chr  "Algeria" "Angola" "Benin" "Botswana" ...
##  $ gdpPercap_1952: num  2449 3521 1063 851 543 ...
##  $ gdpPercap_1957: num  3014 3828 960 918 617 ...
##  $ gdpPercap_1962: num  2551 4269 949 984 723 ...
##  $ gdpPercap_1967: num  3247 5523 1036 1215 795 ...
##  $ gdpPercap_1972: num  4183 5473 1086 2264 855 ...
##  $ gdpPercap_1977: num  4910 3009 1029 3215 743 ...
##  $ gdpPercap_1982: num  5745 2757 1278 4551 807 ...
##  $ gdpPercap_1987: num  5681 2430 1226 6206 912 ...
##  $ gdpPercap_1992: num  5023 2628 1191 7954 932 ...
##  $ gdpPercap_1997: num  4797 2277 1233 8647 946 ...
##  $ gdpPercap_2002: num  5288 2773 1373 11004 1038 ...
##  $ gdpPercap_2007: num  6223 4797 1441 12570 1217 ...
##  $ lifeExp_1952  : num  43.1 30 38.2 47.6 32 ...
##  $ lifeExp_1957  : num  45.7 32 40.4 49.6 34.9 ...
##  $ lifeExp_1962  : num  48.3 34 42.6 51.5 37.8 ...
##  $ lifeExp_1967  : num  51.4 36 44.9 53.3 40.7 ...
##  $ lifeExp_1972  : num  54.5 37.9 47 56 43.6 ...
##  $ lifeExp_1977  : num  58 39.5 49.2 59.3 46.1 ...
##  $ lifeExp_1982  : num  61.4 39.9 50.9 61.5 48.1 ...
##  $ lifeExp_1987  : num  65.8 39.9 52.3 63.6 49.6 ...
##  $ lifeExp_1992  : num  67.7 40.6 53.9 62.7 50.3 ...
##  $ lifeExp_1997  : num  69.2 41 54.8 52.6 50.3 ...
##  $ lifeExp_2002  : num  71 41 54.4 46.6 50.6 ...
##  $ lifeExp_2007  : num  72.3 42.7 56.7 50.7 52.3 ...
##  $ pop_1952      : num  9279525 4232095 1738315 442308 4469979 ...
##  $ pop_1957      : num  10270856 4561361 1925173 474639 4713416 ...
##  $ pop_1962      : num  11000948 4826015 2151895 512764 4919632 ...
##  $ pop_1967      : num  12760499 5247469 2427334 553541 5127935 ...
##  $ pop_1972      : num  14760787 5894858 2761407 619351 5433886 ...
##  $ pop_1977      : num  17152804 6162675 3168267 781472 5889574 ...
##  $ pop_1982      : num  20033753 7016384 3641603 970347 6634596 ...
##  $ pop_1987      : num  23254956 7874230 4243788 1151184 7586551 ...
##  $ pop_1992      : num  26298373 8735988 4981671 1342614 8878303 ...
##  $ pop_1997      : num  29072015 9875024 6066080 1536536 10352843 ...
##  $ pop_2002      : num  31287142 10866106 7026113 1630347 12251209 ...
##  $ pop_2007      : num  33333216 12420476 8078314 1639131 14326203 ...

all.equal(gap_wide, gap_wide_betterID)

## [1] TRUE

There and back again!